Use this codebook for text classification. Return your classifications in a table with one column for text number (the number preceding each text sample) and a column for each label. Use a csv format. 

Each text sample includes two tweets, labeled as ORIGINAL TWEET and RESPONSE TWEET. The RESPONSE TWEET is a direct response to the ORIGINAL TWEET. Classify the RESPONSE TWEET on three dimensions: whether the sentiment is POSITIVE, NEUTRAL, or NEGATIVE towards the account that posted the ORIGINAL TWEET. These three dimensions are mutually exclusive. Every RESPONSE TWEET must be labeled as 1 for either positive, neutral, or negative. 

You are classifying the RESPONSE TWEET's sentiment towards the account that posted the ORIGINAL TWEET specifically. If the ORIGINAL TWEET mentions people, institutions, companies, etc., do not base the classification of the ORIGINAL TWEET's sentiment towards these entities. For example, the following text sample would be classified as POSITIVE = 1 because the RESPONSE TWEET agrees with the ORIGINAL TWEET: "ORIGINAL TWEET:  Finnish president disputes Trump's claim that his country purchased new fighter jets https://t.co/85icbZcL7b https://t.co/OUrX4nATHP RESPONSE TWEET: well, @realDonaldTrump IS a habitual liar &amp; BS artist. I would believe the Finnish President b4 anything the #LiarInChief #Trump had to say https://t.co/tzt6aReWNu". Note that the RESPONSE TWEET has a negative sentiment towards @realDonaldTrump, but the text sample is still classified as positive. Similarly, this text sample should be classified as POSITIVE = 1 because the RESPONSE TWEET is positive towards the ORIGINAL TWEET: "ORIGINAL TWEET:  The New York Post's cover for Thursday calls out Ilhan Omar for trivializing the 9/11 terrorist attacks as "some people did something" https://t.co/xToghXSGw5 RESPONSE TWEET: Omar =  Muslim Traitor, Hates America  &amp; It's  People,  Causes  Division In Congress https://t.co/pY9iy1wsgn".

Here are the text samples:
